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Computer system spots fake expressions of pain better than people

Could detect deceptive actions in realms of security, job screening

04/03/2014|Canadian HR Reporter|Last Updated: 04/03/2014

A joint study by researchers at the University of California, San Diego, the University at Buffalo and the University of Toronto has found that a computer–vision system can distinguish between real or faked expressions of pain more accurately than can humans.

This ability has obvious uses for uncovering pain malingering — fabricating or exaggerating the symptoms of pain for a variety of motives — but the system also could be used to detect deceptive actions in the realms of security, psychopathology, job screening, medicine and law, said the authors.

The study employed two experiments with a total of 205 human observers who were asked to assess the veracity of expressions of pain in video clips of individuals, some of whom were being subjected to the cold presser test in which a hand is immersed in ice water to measure pain tolerance, and some who were faking their painful expressions.

“Human subjects could not discriminate real from faked expressions of pain more frequently than would be expected by chance,” said Mark Frank, professor of communication at the University at Buffalo. “Even after training, they were accurate only 55 per cent of the time. The computer system, however, was accurate 85 per cent of the time.”

The system managed to detect distinctive, dynamic features of facial expressions that people missed, said Marian Bartlett, research professor at the Institute for Neural Computation at the University of California.

“Human observers just aren’t very good at telling real from faked expressions of pain.”

The researchers employed a computer expression recognition toolbox (CERT), an end-to-end system for fully automated facial-expression recognition that operates in real time. They found that machine vision was able to automatically distinguish deceptive facial signals from genuine facial signals by extracting information from spatiotemporal facial-expression signals that humans either cannot or do not extract.